package com.atguigu.day09;

import com.atguigu.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple1;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.AggregateFunction;
import org.apache.flink.table.functions.TableAggregateFunction;
import org.apache.flink.util.Collector;
import org.apache.hadoop.yarn.webapp.hamlet2.Hamlet;

import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.call;

public class Flink06_UDF_TableAggFun {
    public static void main(String[] args) {
        //1.获取流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        env.setParallelism(1);

        //2.从端口读取数据并转为WaterSensor
        SingleOutputStreamOperator<WaterSensor> waterSensorStream = env.socketTextStream("localhost", 9999)
                .map(new MapFunction<String, WaterSensor>() {
                    @Override
                    public WaterSensor map(String value) throws Exception {
                        String[] split = value.split(",");
                        return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
                    }
                });

        //3.获取表的执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        Table table = tableEnv.fromDataStream(waterSensorStream);

        //TODO 4.使用自定义函数查询数据

        //不注册直接使用
  /*      table.
                groupBy($("id"))
                .flatAggregate(call(MyUDTAF.class, $("vc")))
                .select($("id"),$("f0"),$("f1"))
                .execute()
                .print();*/

        //先注册再使用
        tableEnv.createTemporarySystemFunction("top2Vc", MyUDTAF.class);

        table.
                groupBy($("id"))
                .flatAggregate(call("top2Vc", $("vc")).as("value", "rank"))
                .select($("id"),$("value"),$("rank"))
                .execute()
                .print();



    }

    //自定义一个表聚合函数，求最大的两个VC
    public static class MyTopAcc {
        public Integer first;
        public Integer second;
    }

    public static class MyUDTAF extends TableAggregateFunction<Tuple2<Integer,String>, MyTopAcc> {

        @Override
        public MyTopAcc createAccumulator() {
            MyTopAcc topAcc = new MyTopAcc();
            topAcc.first = Integer.MIN_VALUE;
            topAcc.second = Integer.MIN_VALUE;
            return topAcc;
        }

        public void accumulate(MyTopAcc acc, Integer value) {
            if (value>acc.first){
                acc.second = acc.first;
                acc.first = value;
            }else if (value>acc.second){
                acc.second = value;
            }
        }

        public void emitValue(MyTopAcc acc, Collector<Tuple2<Integer,String>> out) {
            if (acc.first!=Integer.MIN_VALUE){
                out.collect(Tuple2.of(acc.first,"1"));
            }

            if (acc.second!= Integer.MIN_VALUE) {
                out.collect(Tuple2.of(acc.second, "2"));
            }
        }

    }
}